5 research outputs found
Incremental file reorganization schemes
Issued as Final project report, Project no. G-36-66
Mission-Phasing Techniques for Constrained Agents in Stochastic Environments.
Resource constraints restrict the set of actions that an agent can take, such that the agent might
not be able to perform all its desired tasks. Computational time limitations restrict the number of
states that an agent can model and reason over, such that the agent might not be able to formulate
a policy that can respond to all possible eventualities. This work argues that, in either
situation, one effective way of improving the agent's performance is to adopt a phasing strategy.
Resource-constrained agents can choose to reconfigure resources and switch action sets for handling
upcoming events better when moving from phase to phase; time-limited agents can choose to focus
computation on high-value phases and to exploit additional computation time during the execution of
earlier phases to improve solutions for future phases.
This dissertation consists of two parts, corresponding to the aforementioned resource constraints
and computational time limitations. The first part of the dissertation focuses on the development
of automated resource-driven mission-phasing techniques for agents operating in
resource-constrained environments. We designed a suite of algorithms which not only can find
solutions to optimize the use of predefined phase-switching points, but can also automatically
determine where to establish such points, accounting for the cost of creating them, in complex
stochastic environments. By formulating the coupled problems of mission decomposition, resource
configuration, and policy formulation into a single compact mathematical formulation, the presented
algorithms can effectively exploit problem structure and often considerably reduce computational
cost for finding exact solutions.
The second part of this dissertation is the design of computation-driven mission-phasing techniques
for time-critical systems. We developed a new deliberation scheduling approach, which can
simultaneously solve the coupled problems of deciding both when to deliberate given its cost, and
which phase decision procedures to execute during deliberation intervals. Meanwhile, we designed a
heuristic search method to effectively utilize the allocated time within each phase. As illustrated
in experimental results, the computation-driven mission-phasing techniques, which
extend problem decomposition techniques with the across-phase deliberation scheduling and
inner-phase heuristic search methods mentioned above, can help an agent generate a better
policy within time limit.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60650/1/jianhuiw_1.pd